People usually identify features of a musical piece so as to categorize similar musical pieces together. In most cases, the categorization is done manually according to a subject's preference. It is known that music has several positive effects on a subject and listening to preferred music will have a greater positive effect on the subject.
One conventional technique is to identify genres or musical characteristics as the features of the musical pieces. It may in many instances result in inaccurate categorization of musical pieces, since there are only limited genres (e.g. jazz and rock) and musical characteristics (e.g. pitch and tempo). Further, a musical song that has a positive effect on a person may not have the same effect on another person.
Thus, it would be beneficial to categorize musical pieces automatically in response to the effect on the subject. However, with the existing techniques and tools, it is difficult to provide a method that effectively categorizes musical pieces to cause a positive effect on a subject.
A need therefore exists to provide a system and method which can be used to automatically derive features that are indicative of a subject state when the subject listens to a musical piece.